Specialization in the iStar2.0 language

iStar2.0 has been proposed as a standard language for building goal- and agent-oriented models. It is an evolution of the former i* language, with the purpose of homogenising existing syntactical and semantic variations of basic i* constructs that researchers in the field introduced along the years. In its first version (2016), iStar2.0 was intentionally kept simple, and some constructs were merely introduced but not formally defined. One of them is the notion of specialization. The specialization relationship is offered by iStar2.0 through the is-a construct defined over actors (subactor x is-a superactor y). Although the overall meaning of this construct is highly intuitive, its semantics when it comes to the fine-grained level of the models is not defined in the standard. In this paper we provide a formal definition of the specialization relationship ready to be incorporated into a next release of the iStar2.0 standard language. We root our proposal over existing work on conceptual modeling in general, and object-orientation in particular. Also, we use the results of a survey that provides some hints about what definition do iStar2.0 modelers expect from specialization. As a consequence of this twofold analysis, we identify, define and specify a set of specialization operations that can be applied over iStar2.0 models. Correctness conditions for them are also formally stated. The result of our work is a formal proposal of specialization for iStar2.0 that allows its use in a well-defined manner and contributes to its standardization.Peer ReviewedPostprint (published version

The notion of specialization in the i*framework

This thesis provides a formal proposal for the specialization relationship in the i* framework that allows its use in a well-defined manner. I root my proposal over existing works in different areas that are interested in representing knowledge: knowledge representation from Artificial Intelligence and conceptual modeling and object-oriented programming languages from Software Development. Also, I use the results of a survey conducted in the i* community that provides some insights about what i* modelers expect from specialization. As a consequence of this twofold analysis, I identify three specialization operations: extension, refinement and redefinition. For each of them, I: - motivate its need and provide some rationale; - distinguish the several cases that can occur in each operation; - define the elements involved in each of these cases and the correctness conditions that must be fulfilled; - demonstrate by induction the fulfilment of the conditions identified for preserving satisfaction; - provide some illustrative examples in the context of an exemplar about travel agencies and travelers. The specialization relationship is offered by the i* framework through the is-a construct defined over actors (a subactor is-a superactor) since it was first released. Although the overall meaning of this construct is highly intuitive, its effects at the level of intentional elements and dependencies are not always clear, hampering seriously its appropriate use. In order to be able to reason about correctness and satisfaction, I define previously the conditions that must be preserved when a specialization takes place. In addition, I provide a methodology with well-defined steps that contextualize the formal aspects of this thesis in a development process. As a conclusion, this thesis is making possible the use of the specialization relationship in i* in a precise, non-ambiguous manner

Ontology-based methodology for error detection in software design

Improving the quality of a software design with the goal of producing a high quality software product continues to grow in importance due to the costs that result from poorly designed software. It is commonly accepted that multiple design views are required in order to clearly specify the required functionality of software. There is universal agreement as to the importance of identifying inconsistencies early in the software design process, but the challenge is how to reconcile the representations of the diverse views to ensure consistency. To address the problem of inconsistencies that occur across multiple design views, this research introduces the Methodology for Objects to Agents (MOA). MOA utilizes a new ontology, the Ontology for Software Specification and Design (OSSD), as a common information model to integrate specification knowledge and design knowledge in order to facilitate the interoperability of formal requirements modeling tools and design tools, with the end goal of detecting inconsistency errors in a design. The methodology, which transforms designs represented using the Unified Modeling Language (UML) into representations written in formal agent-oriented modeling languages, integrates object-oriented concepts and agent-oriented concepts in order to take advantage of the benefits that both approaches can provide. The OSSD model is a hierarchical decomposition of software development concepts, including ontological constructs of objects, attributes, behavior, relations, states, transitions, goals, constraints, and plans. The methodology includes a consistency checking process that defines a consistency framework and an Inter-View Inconsistency Detection technique. MOA enhances software design quality by integrating multiple software design views, integrating object-oriented and agent-oriented concepts, and defining an error detection method that associates rules with ontological properties

A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

Sensors play an increasingly critical role in capturing and distributing observations of phenomena in our environment. The vision of the semantic sensor web is to enable the interoperability of various applications that use sensor data provided by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping and that can adapt to the dynamic metadata of sensors are required. Semantic mapping for the sensor web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few tasks. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial, and temporal elements that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support the resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings

Ontological analysis of means-end links

The i* community has raised several main dialects and dozens of variations in the definition of the i* language. Differences may be found related not just to the representation of new concepts but to the very core of the i* language. In previous work we have tackled this issue mainly from a syntactic point of view, using metamodels and syntactic-based model interoperability frameworks. In this paper, we go one step beyond and consider the use of foundational ontologies in general, and UFO in particular, as a way to clarify the meaning of core i* constructs and as the basis to propose a normative definition. We focus here on one of the most characteristics i* constructs, namely means-end links.Postprint (published version

Self-adaptive Software Modeling Based on Contextual Requirements

The ability of self-adaptive software in responding to change is determined by contextual requirements, i.e. a requirement in capturing relevant context-atributes and modeling behavior for system adaptation. However, in most cases, modeling for self-adaptive software is does not take into consider the requirements evolution based on contextual requirements. This paper introduces an approach through requirements modeling languages directed to adaptation patterns to support requirements evolution. The model is prepared through contextual requirements approach that is integrated into MAPE-K (monitor, anayze, plan, execute - knowledge) patterns in goal-oriented requirements engineering. As an evaluation, the adaptation process is modeled for cleaner robot. The experimental results show that the requirements modeling process has been able to direct software into self-adaptive capability and meet the requirements evolution